Summary/Abstract Substance use disorders have a profound impact on human health and wellbeing. The development of new treatments for these disorders has remained difficult due to the complexity of the neural circuits and the underlying genetic mechanisms. Recent genome-wide association studies have begun to identify a few of loci associated with the predisposition to addiction, with many more loci are implicated with lower confidence. The majority of the genetic variants associated with complex brain disorders, including substance use, are likely to be located in non-coding regulatory regions, particularly enhancers, and not within protein-coding genes. Despite the importance of enhancer regions in the brain, the computational and experimental tools to study their function are still in their infancy. To work towards deciphering this critical biological mechanism underlying substance use disorders, we seek to build a framework to study the function of both human and mouse brain enhancer regions in vivo. First, we will analyze publically available genetic and epigenetic data sets to identify human genetic variation at enhancers regions that is likely to influence substance use predisposition. Next, we will measure the impact of that genetic variation using a high-throughput reporter assay, which has the ability to simultaneously test the activity of thousands of enhancers across the mouse brain in vivo under different conditions. Finally, we will validate those predictions using CRISPR interference on conserved orthologous enhancers in the mouse. In an initial test case, we will apply our methods to interpret a genome-wide association study of smoking behavior using cell type-specific human epigenomics. The key candidates that result from the computational analysis will be screened and validated in a mouse model of nicotine exposure. The result of our research effort will be a map that links high-confidence and low-confidence substance use-associated genetic variation to neural enhancer function. Furthermore, the flexible computational and experimental framework has the potential to study any combination of candidate enhancers and genetic variants in the context of different brain regions, different cell types, and different animal models.